International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 2014 1 ISSN 2250-3153

Analysis of Link Algorithms for Web Mining

Monica Sehgal

Abstract- As the use of Web is increasing more day by day, the 4) Much of web information is semi structured web users get easily lost in the web’s rich hyper structure. The 5) Much of the web information is linked main aim of the owner of the website is to provide the relevant 6) Availability of redundant web information. information to the users to fulfill their needs. Web mining 7) The web is noisy technique is used to categorize users and pages by analyzing 8) The web is also about services users behavior, the content of pages and order of URLs accessed. 9) The web is dynamic Web Structure Mining plays an important role in this approach. 10) The web is a virtual Society. In this paper we discuss and compare the commonly used algorithms i.e. PageRank, Weighted PageRank and HITS. This paper is organized as follows: Web Mining is introduced in Section II. The Taxonomy of Web Miningnamely Web Index Terms- Web Mining, Web Content Mining, Web Structure Content Mining, Web Structure Mining, Web usage Mining are Mining, Web Usage Mining, PageRank, Weighted PageRank and discussed in Section III. Section IV describes the various Link HITS. analysis algorithms. Section IV(A) defines Page Rank Algorithm, IV(B) defines Weighted Page Rank Algorithm and IV(C) defines Induced Topic Search Algorithm . I. INTRODUCTION Section V provides the comparison of various Link Analysis he (WWW) is rapidly growing on all Algorithms. T aspects and is a massive, explosive, diverse, dynamic and mostly unstructured repository of data. Till now, WWW is the huge information repository referenced for Knowledge. II. WEB MINING Generally, user faces a lot of challenges in the Web: Web Mining is the use of techniques to 1) Huge amount of information on the web automatically discover and extract information from Web 2) Web information coverage is very wide and diverse documents and services. 3) All types information/data must exist on the web

Web Mining Process :The Complete process of extracting knowledge from Web data [5][8] is as follows in Figure-1:

Representatio Mining DATA Tools PATTE n KNOWLE RN DGE & Visualization

Figure 1: Web Mining Process

Web Mining can be decomposed into several tasks, namely: Web Content Mining (WCM) is responsible for exploring 1. Resource Finding refers to the task of getting/ the proper and relevant information from the contents of web. It reading intended web credentials. focuses mainly inner document level. 2. Information selection and pre-processing refers Web Structure Mining (WSM) is the process by which we to Robotically selecting and pre-processing definite discover the model of link structure of the web pages. We catalog from information retrieved Web resources. the links; generate information like the similarity and relations 3. Generalization refers to Robotically ascertains among them by taking advantage of Hyperlink topology. Its Goal certain general patterns at individual or multiple is to generate structured summary about the website and web Web Situates . page. 4. Analysis: Mined Pattern Rationale and Web usage Mining (WUM) is responsible for recording the interpretation. user profile and user behavior inside the log file of the web.

III. WEB MINING CATEGORIES Web Mining areas according to the Web data used as input as follows:

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Table 1 gives an overview of above Web Mining categories.

Web Mining Web Content Mining Web Structure Web usage IR View DB View Mining Mining View of - UnStructured - Semi – - Link - Interactivi Data - Structured Structured Structure ty - Web Site as DB Main Data - Text - - Link - Server Documents Documents Structure Logs - HyperText - Browser Documents Logs Representat - Bag of - Edge labeled - Graph - Relational ion Words, n- Graph, Table gram Terms, - Relational - Graph - Phrases, Concepts or ontology - Relational Method - Machine - Proprietary - Proprietar - Algorithms y learning - Statistical - Association algorithms - Statistical (including Rules - Associatio NLP) n rules Application - Categorizatio - Finding - Categoriz - Site Categories n frequent Sub ation Constructi - Clustering Structures - Clustering on - Finding - Web Site - Adaptatio extract rules Schema n and - Finding Discovery managem patterns in ent Text - Marketing - User Modeling

rank to adjust results so that more “Significant” pages move up in the results page of a user’s search result display. IV. LINK ANALYSIS ALGORITHMS The Page rank value for a page is calculated based on the Web mining techniques provides the additional information number of backlinks to a page. Page Rank is displayed if you’ve through where different documents are connected.[2] installed the toolbar (http://toolbar.google.com/). And its We can view the web as a directed labeled graph whose nodes rank only goes from 0 – 10 and seems to be like a logarithmic are the documents or pages and edges are the hyperlinks between scale: them. This directed graph structure is known as web graph. There Toolbar PageRank Real PageRank are number of algorithms proposed based on link analysis. Three (Log base 10) important algorithms Page rank[5], Weighted Page rank[6] and 0 0 - 100 HITS[7] are discussed below: 1 100 – 1,000 2 1,000 – 10,000 A. Page Rank 3 10,000 – 100,000 Page Rank, an algorithm developed by Brin and Page at 4 And so on…. Stanford University, is a numeric value that represents how important a page is on the web. Page Rank is the method used by Following are some of the terms used: Google for measuring a page’s “Significance”. After considering (1) PageRank (PR) : the actual, real, page all other factors like Title Tag and Keywords , Google uses Page rank for each page as calculated by Google.

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(2) TOOLBAR PR : The Page rank displayed 4. d(… - All these fractions of votes are in the Google toolbar in your browser added together but, to stop the other ranges from 0 to 10. pages having too much influence, this (3) BACKLINK : If page A links out to page total vote is “drown” by multiplying it B, then page B is said to have a “back by 0.85 (the factor “d”). link” from page A. We can’t know the exact details of the scale because the B. Weighted Page Rank maximum PR of all pages on the web changes every month when The more popular web pages are the more linkages that other Google does its re-indexing! If we presume the scale is web pages tend to have to them or are linked to by them. The logarithmic then Google could simply give the highest actual PR proposed extended Page Rank algorithm – a Weighted Page page a toolbar of 10 and scale the rest appropriately. Rank Algorithm[10] – assigns larger rank values to more The another definition given by Google is as follows: We important (popular) pages instead of dividing the rank value of a assume page A has pages T1…Tn which points to it. The page evenly among its out link pages. Each out link page gets a parameter d (damping factor), can be set between 0 and 1. But value proportional to its popularity (its number of in links and usually set to 0.85. C(A) refers to the number of links going out out links). The popularity from the number of in links and out of page A. The Page rank of a page A is given as follows: links is recorded as Win(v,u) and Wout(v,u), respectively. Win(v,u) is the weight of link(v,u) calculated based on the number of in PR(A) = (1 – d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn)) links of page u and the number of in links of all reference pages of page v. Note that Page Ranks form a probability distribution over web pages, so the sum of all web pages’ Page Ranks will be one. Win(v,u) = Page rank or PR(A) has the following sections: 1. PR (Tn) – Each page has a notion of Where Iu and Ip represent the number of in links of page u its own self-importance. That’s and page p, respectively. R(v) denotes the reference page list of “PR(T1)” for the first page in the web page v. all the way up to “PR(Tn)” for the last page. Wout(v,u) is the weight of link(v,u) calculated based on the 2. C (Tn) – Each page spreads its vote number of out links of page u and the number of out links of all out evenly amongst all of it’s reference pages of page v. outgoing links. The count , or the number , of outgoing links for page 1 Wout(v,u) = is “C (T1)” , “C (Tn)” for page n, and so on for all pages. Where Iu and Ip represent the number of out links of page u 3. PR (Tn)/C (Tn) – so if out page and page p, respectively. R(v) denotes the reference page list of (page A) has a backlink from page “n” page v. Figure-1 shows an example of some links of a the share of the vote page A will get is hypothetical website. “PR(Tn)/C(Tn)”.

P1 A P2

B P3

Figure 1: Links of a Web Site 2. Relevant Pages (R): These Pages are relevant but not having important PageRank VS Weighted PageRank information about a given query. In order to compare the WPR with the PageRank, the 3. Weakly relevant Pages (WR): These resultant pages of a query are categorized into four categories Pages may have the query Keywords based on their relevancy to the given query. [8] They are but they do not have the relevant 1. Very Relevant Pages (VR): These are information. the pages that contain very important 4. Irrelevant Pages (IR) : These Pages information related to a given query. are not having any relevant information and query Keywords.

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Where, v1,v2,v3 and v4 are the values assigned to a page if The PageRank and WPR algorithms both provide ranked the page is VR,R,WR,IR respectively. These values are always pages in the sorting order to users based on the given query. So, v1>v2>v3>v4. Experimental studies show that WPR produces in the resultant list, the number of relevant pages and their order larger relevancy values than the PageRank. are very important for users. Relevance Rule is used to calculate the relevancy value of each page in the list of pages. That makes C. HITS (Hyperlink Induced Topic Search) WPR different from PageRank. Kleinberg developed a WSM based algorithm named Hyperlink Induced Topic Search (HITS) in 1988 which identifies Relevancy Rule: The Relevancy of page to a given query two different forms of web pages called hubs and authorities. depends on its category and its position in the page –list. The Authorities are pages having important contents. Hubs are pages larger the relevancy value, the better is the result. that act as resource lists, guiding users to authorities. Thus, a good hub page for a subject points to many authoritative pages on that content, and a good authority page is pointed by many fine hub pages on the same subject. Where, Kleinberg states that a page may be a good hub and a good i = ith page in the result page –list authority at the same time [8, 9]. This spherical relationship leads R(p), n = the first n pages chosen from the list R(p) to the definition of an iterative algorithm called HITS. Wi = weight of ith page as given below The HITS algorithm treats WWW as a directed graph G (V, E), where V is a set of vertices representing pages and E is a set of edges that match up to links. Figure 2 shows the hubs and authorities in web [2].

Hubs Authorities

Figure 2: Hubs and Authorities 3. For every hub p It has two steps, ϵ H 1. Sampling Step – In this step a set of 4. relevant pages for the given query are collected. 2. Iterative Step – In this step Hubs and Authorities are found using the output of 5. For every sampling step. authority p ϵ A 6. In this HITS Algorithm, the weights of the Hub (Hp) and the weights of the Authority (Ap) are calculated using following algorithm.

HITS Algorithm 7. Normalize 1. Initialize all weights to 1 Where Hq is Hub Score of a page, Aq is authority score of a 2. Repeat Until page, I(p) is set of reference pages pf page p and B(p) is set of the weights referrer pages pf page p. The authority weight of a page is converge: proportional to the sum of hub weights of pages that link to it.

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Similarly a hub of a page is proportional to the sum of authority Engine Model weights of pages that it links to.

Following are some constraints of HITS algorithm VI. CONCLUSION  Hubs and authorities: It is not easy to Web Mining is powerful technique used to extract the distinguish between hubs and information from past behavior of users. Various Algorithms are authorities because many sites are used to rank the relevant pages. PageRank, Weighted PageRank hubs as well as authorities. and HITS treat all links equally when distributing the rank score.  Topic drift: Sometime HITS may not PageRank and weighted PageRank are used in WSM. HITS are produce the most relevant documents used in both WSM and WCM. PageRank and Weighted to the user queries because of PageRank calculates the score at indexing time and sort them equivalent weights. according to importance pf page where as HITS calculates the  Automatically generated links: HITS hub and authority score of n highly relevant pages. The input gives equal importance for parameters used in Page Rank are BackLinks, Weighted automatically generated links which PageRank uses BackLinks and Forward Links as Input may not have relevant topics for the Parameter, HITS uses BackLinks, Forward Link and Content as user query. Input Parameters. Complexity of PageRank Algorithm is O(log  Efficiency: HITS Algorithm is not N) where as complexity of Weighted PageRank and HITS efficient in real time. algorithms are

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